# Requirement Engineering Challenges for AI-intense Systems Development

arXiv: https://arxiv.org/abs/2103.10270 · HTML: https://mlsanity.com/paper/2103.10270/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry](https://mlsanity.com/paper/1812.05389/) (2018)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks](https://mlsanity.com/paper/2001.08001/) (2020)
- [Design Science Research Process: A Model for Producing and Presenting Information Systems Research](https://mlsanity.com/paper/2006.02763/) (2020)
- [Boundary Objects and their Use in Agile Systems Engineering](https://mlsanity.com/paper/1904.12131/) (2019)
- [T-Reqs: Tool Support for Managing Requirements in Large-Scale Agile System Development](https://mlsanity.com/paper/1805.02769/) (2018)
- [LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing](https://mlsanity.com/paper/1912.01563/) (2019)
- [Agility is responsiveness to change: An essential definition](https://mlsanity.com/paper/1909.10082/) (2019)
- [Why and How to Balance Alignment and Diversity of Requirements Engineering Practices in Automotive](https://mlsanity.com/paper/2001.01598/) (2020)
- [Charting Coordination Needs in Large-Scale Agile Organisationswith Boundary Objects and Methodological Islands](https://mlsanity.com/paper/2005.05747/) (2020)
- [Constructive Master's Thesis Work in Industry: Guidelines for Applying Design Science Research](https://mlsanity.com/paper/2012.04966/) (2020)

## Built on by (most cited first, 6 of 6)

- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)
- [A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems](https://mlsanity.com/paper/2212.13570/) (2022)
- [RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers](https://mlsanity.com/paper/2207.08857/) (2022)
- [Requirements Engineering for Older Adult Digital Health Software: A Systematic Literature Review](https://mlsanity.com/paper/2411.03656/) (2024)
- [Towards User-Centred Design of AI-Assisted Decision-Making in Law Enforcement](https://mlsanity.com/paper/2504.17393/) (2025)

## Abstract (arXiv)

Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.